Update main.py

main.py

@@ -1,47 +1,22 @@
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
 from pydantic import BaseModel
+from transformers import pipeline
 
-from .ConfigEnv import config
-from fastapi.middleware.cors import CORSMiddleware
+app = FastAPI(debug=True, title="Text classifier")
 
-from langchain.llms import Clarifai
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-from TextGen import app
 
-class Generate(BaseModel):
-    text:str
+class Payload(BaseModel):
+    text: str
 
-def generate_text(prompt: str):
-    if prompt == "":
-        return {"detail": "Please provide a prompt."}
-    else:
-        prompt = PromptTemplate(template=prompt, input_variables=['Prompt'])
-        llm = Clarifai(
-            pat = config.CLARIFAI_PAT,
-            user_id = config.USER_ID,
-            app_id = config.APP_ID, 
-            model_id = config.MODEL_ID,
-            model_version_id=config.MODEL_VERSION_ID,
-        )
-        llmchain = LLMChain(
-            prompt=prompt,
-            llm=llm
-        )
-        llm_response = llmchain.run({"Prompt": prompt})
-        return Generate(text=llm_response)
 
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
+async def classify_text(text):
+    pipe = pipeline("text-classification",
+                    model="SamLowe/roberta-base-go_emotions")
+    return pipe(text)
 
-@app.get("/", tags=["Home"])
-def api_home():
-    return {'detail': 'Welcome to FastAPI TextGen Tutorial!'}
 
-@app.post("/api/generate", summary="Generate text from prompt", tags=["Generate"], response_model=Generate)
-def inference(input_prompt: str):
-    return generate_text(prompt=input_prompt)
+@app.post("/classify")
+async def classify(payload: Payload):
+    result = await classify_text(payload.text)
+    return JSONResponse({"data": result})